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Showing papers in "Catena in 2022"


Journal ArticleDOI
01 Jan 2022-Catena
TL;DR: In this article, the authors used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR), to predict soil organic carbon (SOC) in an arid agroecosystem in Iran.
Abstract: In the digital soil mapping (DSM) framework, machine learning models quantify the relationship between soil observations and environmental covariates. Generally, the most commonly used covariates (MCC; e.g., topographic attributes and single-time remote sensing data, and legacy maps) were employed in DSM studies. Additionally, remote sensing time-series (RST) data can provide useful information for soil mapping. Therefore, the main aims of the study are to compare the MCC, the monthly Sentinel-2 time-series of vegetation indices dataset, and the combination of datasets (MCC + RST) for soil organic carbon (SOC) prediction in an arid agroecosystem in Iran. We used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR). A total of 237 soil samples at 0–20 cm depths were collected. The 5-fold cross-validation technique was used to evaluate the modeling performance, and 50 bootstrap models were applied to quantify the prediction uncertainty. The results showed that the Cubist model performed the best with the MCC dataset (R2 = 0.35, RMSE = 0.26%) and the combined dataset of MCC and RST (R2 = 0.33, RMSE = 0.27%), while the RF model showed better results for the RST dataset (R2 = 0.10, RMSE = 0.31%). Soil properties could explain the SOC variation in MCC and combined datasets (66.35% and 50.82%, respectively), while NDVI was the most controlling factor in the RST (50.22%). Accordingly, results showed that time-series vegetation indices did not have enough potential to increase SOC prediction accuracy. However, the combination of MCC and RST datasets produced SOC spatial maps with lower uncertainty. Therefore, future studies are required to explicitly explain the efficiency of time-series remotely-sensed data and their interrelationship with environmental covariates to predict SOC in arid regions with low SOC content.

54 citations


Journal ArticleDOI
01 Feb 2022-Catena
TL;DR: In this article, the authors used both proximally and remotely sensed digital data to predict topsoil and subsoil clay at district scale by comparing; they found that the importance of the digital data was most related to the apparent soil electrical conductivity (ECa) and the slope.
Abstract: Accurate prediction of clay is the basis for soil quality assessment and decision making in land use because it governs soil moisture and fertility dynamics. However, using laboratory methods to determine clay across a large district and at multiple depths is tedious and expensive. An alternative is to use proximally and remotely sensed digital data, that can be coupled to laboratory measured clay through models. This study aims to predict topsoil (0–0.3 m) and subsoil (0.9–1.2 m) clay at district scale by comparing; i) importance of proximally (i.e., apparent soil electrical conductivity – ECa) and remotely (i.e., γ-ray spectrometry, digital elevation model – DEM) sensed data, ii) models including a linear mixed model (LMM) and machine learning models (MLs, i.e., Cubist, random forest [RF], support vector machine regression [SVMR], quantile regression forests [QRF], extreme gradient boosting [XGBoost] and bagEarth), iii) two model averaging techniques (i.e., Granger–Ramanathan averaging (GRA) and Lin’s concordance (LCCC) weights) from the top four best models, and iv) uncertainty of the prediction. The results showed that the γ-ray data was most important for topsoil clay prediction, while in the subsoil the slope was most important. Moreover, for topsoil clay prediction the RF was best with fair accuracy (RPD = 1.64), followed by QRF (1.62), Cubist (1.61) and LMM (1.55) which outperformed bagEarth (1.51), SVMR (1.47) and XGBoost (1.47). For the subsoil, all seven models achieved poor accuracy (RPD

44 citations


Journal ArticleDOI
01 Feb 2022-Catena
TL;DR: In this article , the authors compared the importance of proximal and remote data for the prediction of topsoil and subsoil clay at district scale by comparing the results of proximally and remotely sensed digital data, which can be coupled to laboratory measured clay through models.
Abstract: • Models were compared for fusion of proximal and remote data for clay prediction. • γ-ray and DEM data was the most important for the topsoil and subsoil prediction. • Model averaging was proposed to improve prediction ability of calibration models. • The GRA was suggested as protocol for clay prediction at district scale. Accurate prediction of clay is the basis for soil quality assessment and decision making in land use because it governs soil moisture and fertility dynamics. However, using laboratory methods to determine clay across a large district and at multiple depths is tedious and expensive. An alternative is to use proximally and remotely sensed digital data, that can be coupled to laboratory measured clay through models. This study aims to predict topsoil (0–0.3 m) and subsoil (0.9–1.2 m) clay at district scale by comparing; i) importance of proximally (i.e., apparent soil electrical conductivity – EC a ) and remotely (i.e., γ-ray spectrometry, digital elevation model – DEM) sensed data, ii) models including a linear mixed model (LMM) and machine learning models (MLs, i.e., Cubist, random forest [RF], support vector machine regression [SVMR], quantile regression forests [QRF], extreme gradient boosting [XGBoost] and bagEarth), iii) two model averaging techniques (i.e., Granger–Ramanathan averaging (GRA) and Lin’s concordance (LCCC) weights) from the top four best models, and iv) uncertainty of the prediction. The results showed that the γ-ray data was most important for topsoil clay prediction, while in the subsoil the slope was most important. Moreover, for topsoil clay prediction the RF was best with fair accuracy (RPD = 1.64), followed by QRF (1.62), Cubist (1.61) and LMM (1.55) which outperformed bagEarth (1.51), SVMR (1.47) and XGBoost (1.47). For the subsoil, all seven models achieved poor accuracy (RPD < 1.4) with RF (1.34) again being the best. The 90% prediction interval was larger in the subsoil compared to topsoil. Furthermore, while both averaging methods improved the prediction in both depths, the improvement using GRA was more pronounced. Therefore, we recommend the GRA be adopted as a protocol for district-scale clay prediction.

44 citations


Journal ArticleDOI
01 Feb 2022-Catena
TL;DR: Li et al. as discussed by the authors examined the response of plant and soil parameters to grazing exclusion, a 32-year field experiment was conducted in active dune systems in the Horqin Sandy Land, and showed that the dominant species changed significantly at the windward and leeward sides of dunes, and at interdune lowlands after longterm grazing exclusion.
Abstract: Grazing exclusion is an important policy currently being employed by the Chinese government to recover degraded grasslands. Despite many field experiments, controversy still exists concerning the effects of grazing exclusion on the restoration of sand dune ecosystems. In order to examine the response of plant and soil parameters to grazing exclusion, a 32-year field experiment was conducted in active dune systems in the Horqin Sandy Land. The results showed that the dominant species changed significantly at the windward and leeward sides of dunes, and at interdune lowlands after long-term grazing exclusion. Plant density, cover, species richness, and soil organic carbon and total nitrogen (N) significantly increased across all topographic locations in areas with grazing exclusion. The effects of grazing exclusion on plant and soil parameters varied as a function of position in the dune system. In general, the recovery of plant and soil parameters occurred more rapidly at the windward side than at the leeward side when grazers were excluded. Soil organic carbon and total N were positively correlated with plant community density, cover, and species richness in active and stabilized sand dune systems. In addition, grazing exclusion strengthened the relationship between soil and plant parameters. The results showed that the effects of grazing exclusion on plant and soil properties were strongly dependent on dune position. These findings should prompt those responsible to assess the recovery of sand dune systems by synthesizing the effects of multiple positions within a dune system.

42 citations


Journal ArticleDOI
01 Feb 2022-Catena
TL;DR: Li et al. as mentioned in this paper examined the response of plant and soil parameters to grazing exclusion, a 32-year field experiment was conducted in active dune systems in the Horqin Sandy Land, and showed that the dominant species changed significantly at the windward and leeward sides of dunes, and at interdune lowlands after longterm grazing exclusion.
Abstract: Grazing exclusion is an important policy currently being employed by the Chinese government to recover degraded grasslands. Despite many field experiments, controversy still exists concerning the effects of grazing exclusion on the restoration of sand dune ecosystems. In order to examine the response of plant and soil parameters to grazing exclusion, a 32-year field experiment was conducted in active dune systems in the Horqin Sandy Land. The results showed that the dominant species changed significantly at the windward and leeward sides of dunes, and at interdune lowlands after long-term grazing exclusion. Plant density, cover, species richness, and soil organic carbon and total nitrogen (N) significantly increased across all topographic locations in areas with grazing exclusion. The effects of grazing exclusion on plant and soil parameters varied as a function of position in the dune system. In general, the recovery of plant and soil parameters occurred more rapidly at the windward side than at the leeward side when grazers were excluded. Soil organic carbon and total N were positively correlated with plant community density, cover, and species richness in active and stabilized sand dune systems. In addition, grazing exclusion strengthened the relationship between soil and plant parameters. The results showed that the effects of grazing exclusion on plant and soil properties were strongly dependent on dune position. These findings should prompt those responsible to assess the recovery of sand dune systems by synthesizing the effects of multiple positions within a dune system.

42 citations


Journal ArticleDOI
01 May 2022-Catena
TL;DR: In this paper , a bootstrap hybrid machine learning framework was developed combine Sentinel-2 data and environmental covariates, which was applied to input the spectral information and environmental variables to identify the primary factors influencing soil salinity.
Abstract: Soil salinization is the main source of global soil degradation. It has impeded progress towards sustainable development goals (SDGs) by threatening 20% of irrigated areas. However, in many data-poor regions, accurate soil salinization information is unavailable. Thus, an updated soil salinity map with high accuracy and resolution is urgently needed to help local governments conduct precise management. In this study, a bootstrap hybrid machine learning framework was developed combine Sentinel-2 data and environmental covariates. The Boruta algorithm was applied to input the spectral information and environmental variables to identify the primary factors influencing soil salinity. By averaging 100 model iterations within a bootstrap framework, the soil salinity mapping outcomes were compared from four machine learning methods (bagging, classification and regression tree, random forest, and gradient boosting regression tree (GBRT)). The results showed that the models driven by spectral information and environmental covariates (strategy II) explained 68∼88% of the variability in soil salinity. The model accuracy of strategy II was improved by 5%-8% over that of the models driven only by spectral information (strategy I). The GBRT yielded the most appropriate averaging performance of the four machine learning approaches within strategy II, with an R2 of 0.88, a root mean square error (RMSE) of 6.33 dS m−1, a ratio of performance to interquartile distance (RPIQ) of 4.66 and model stability (ROB) of 0.44. The bootstrap averaging method was relatively stable and had high accuracy potential. The distribution of soil salinity in the Ebinur Lake region was the result of a combination of natural and human activity influences. The proposed approach provides a soil salinity mapping strategy with a fine resolution (10 m) and high accuracy in data-poor places. It may also aid in the restoration of biodiversity, the decrease in land degradation, and the avoidance of future food output reductions in the future.

36 citations


Journal ArticleDOI
01 Jun 2022-Catena
TL;DR: In this article , the authors proposed an approach that combines the Transient Rainfall Infiltration and Grid-Based Regional Slope Stability (TRIGRS) model and the Rapid Mass Movements Simulation (RAMMS) model to achieve hourly hazard prediction.
Abstract: Landslides, debris flows, and other destructive natural hazards induced by heavy rainfall in mountainous regions are sometimes not independent but combined to form a disaster chain. Based on the integral link between the triggering of the landslide and the subsequent debris flow, we propose an approach that combines the Transient Rainfall Infiltration and Grid-Based Regional Slope Stability (TRIGRS) model and the Rapid Mass Movements Simulation (RAMMS) model to achieve hourly hazard prediction. The results indicate that the TRIGRS model performed well in predicting the spatial distribution of the shallow landslides, with a success rate of 81.86%. Thus, it is reasonable to use it as the initial input for debris flow simulations. The relationship between the landslide area and the accumulated rainfall obtained using the TRIGRS model is a power-law relationship, which provides a reference for regions that lack rainfall data to predict the material source of a debris flow. The coupled model was found to have a good accuracy of 76.77% in simulating the debris flow. This was close to the debris flow simulation based on the interpreted landslides, and it still produced reasonable results and a more practical value. Furthermore, the proposed coupled model can dynamically predict disasters by the hour based on actual rainfall events. Therefore, the results of this study help provide a more complete hazard prediction picture for rainfall-induced landslide-debris flow hazards in mountainous regions.

31 citations


Journal ArticleDOI
01 Jun 2022-Catena
TL;DR: In this paper , a review of the current possibilities of using zeolites in agriculture, mainly for the production of fertilisers, is presented, where the authors show that the use of zeolite can not only serve as a sorbent for pollutants in the environment, but also as a reservoir of water and nutrients for plants.
Abstract: Zeolites are porous aluminosilicates with a crystalline structure that contain a system of interconnected chambers and channels. The geometrical parameters of zeolites are one of the most important characteristics responsible for their adsorption capacity. As a result, zeolites can not only serve as a sorbent for pollutants in the environment, but also as a reservoir of water and nutrients for plants (anions and cations). Due to their unique properties, zeolites have become more and more popular in recent years and find practical application in many branches of the economy. The study results to date prove that zeolites are safe for the environment and living organisms, and their multidirectional use in agriculture results primarily from their high porosity, sorption-ion-exchange capacity and well-developed specific surface area. A direct application of zeolites to soil not only has a beneficial effect on the soil sorption capacity, but also reduces soil acidification and increases the efficiency of nutrient use. Better utilisation of nutrients from fertilisers gives higher yields and reduces nutrient dispersion in the environment. Another advantage of zeolites is that they can be obtained by synthesis from various waste materials (e.g. ashes), making their production cost relatively low. This meets the principles of sustainable development and is part of the closed-loop economy and the retardation of environmental resources. Given that zeolites are the subject of many researches, the present study was prepared as a review of the current possibilities of using these materials in agriculture, mainly for the production of fertilisers.

29 citations


Journal ArticleDOI
01 Feb 2022-Catena
TL;DR: Wang et al. as mentioned in this paper used the interferometric synthetic aperture radar (InSAR) technique and landslide and fissure spatiotemporal statistics to investigate the spreading process of the slow subsidence caused by underground mining and examined its impact on the occurrence of shallow landslides.
Abstract: Surface subsidence caused by underground coal mining affects the hillslope stability conditions. However, few studies have focused on the coupling relationship between slow surface subsidence and landslide occurrences. A detailed landslide and fissure inventory in a coal mining area in Shaanxi Province, China, was produced based on interpretation of multitemporal satellite images and unmanned aerial vehicle (UAV) surveys. We used the interferometric synthetic aperture radar (InSAR) technique and landslide and fissure spatiotemporal statistics to investigate the spreading process of the slow subsidence caused by underground mining and examined its impact on the occurrence of shallow landslides. The InSAR results indicate that the actual extent of the subsidence zone is larger than the range of underground mining, which formed a subsidence basin along the coal mining panels. The subsidence curves go through initial, accelerative, and slow subsidence stages and characterized by S-shaped, which can be adequately fitted with logistic regression. Moreover, subsidence does not cease after the end of coal exploitation. Logistic models predicted that the duration of residual subsidence reached about 2–3 years. Subsidence significantly increased the likelihood of landslide occurrences. The spatial pattern of landslides is associated with the actual coal mining. We also investigated the clustering phenomenon of landslides and fissures under the impacts of subsidence. The frequency ratio of landslides and fissures increased with the cumulative subsidence. Finally, we propose a schematic view for landslides caused by coal mining and precipitation. This study will be helpful for elucidating the spatial–temporal evolution of slow subsidence and its impact on loess landslides in coal mining area.

29 citations


Journal ArticleDOI
Yunrui Ma1, Qingyu Guan1, Yunfan Sun1, Jun Zhang1, Liqin Yang1, Enqi Yang1, Huichun Li1, Qinqin Du1 
01 Jan 2022-Catena
TL;DR: In this paper, the authors explored the time-lag and time-accumulation effects of the normalized vegetation index (NDVI) response to climate factors (precipitation, temperature), identified the main controlling factors that influence the variation of NDVI, and found that considering the optimal time effect is of great significance.
Abstract: Understanding the trend of vegetation change and its reaction to climate variation is important for revealing the mechanism of ecosystem behavior. However, current research rarely systematically analyzes the time effects of climate variation on vegetation dynamics (time-lag and time-accumulation effects), especially in arid and semi-arid mountainous terrain. The typical mountainous terrain—the Qilian Mountains was taken as the study area, and the spatiotemporal changes and vertical zonality distributions of the normalized vegetation index (NDVI) were explored. This study explored the time-lag and time-accumulation effects of the NDVI response to climate factors (precipitation, temperature), identified the main controlling factors that influence the variation of NDVI. The results show that in the growing season from 2000 to 2019, the NDVI represented an overall upward trend, especially in the northwest, and the growth rate of NDVI at low-altitude was greater. The time-accumulation effect of precipitation has an obvious effect on vegetation, especially on deserta and meadow; and the time-lag and time-accumulation effects of temperature have an obvious influence. Regarding the climate-vegetation response mechanism, this study finds that considering the optimal time effect is of great significance. In addition, compared with precipitation, the temperature has a more significant promotion effect on vegetation growth in the Qilian Mountains. The above results indicate that when the existing climate models study vegetation-climate interactions, considering the time effects of vegetation response to climate is of great significance for accurately monitoring vegetation dynamics under environmental changes.

28 citations


Journal ArticleDOI
01 Apr 2022-Catena
TL;DR: In this article , the contamination of six heavy metals (Cr, Ni, Cu, Zn, Cd and Pb) in 42 river sediment samples was investigated in the Oder and Vistula, the two biggest rivers in Poland.
Abstract: The contamination of six heavy metals (Cr, Ni, Cu, Zn, Cd and Pb) in 42 river sediment samples was investigated in the Oder and Vistula, the two biggest rivers in Poland. This is the first research which considers almost the whole area of Poland (96.3%) to obtain the overall characteristics of heavy metals (HMs), their spatial distribution, pollution levels and their possible sources. The degree of pollution in sediments was calculated by several geochemical indices (geoaccumulation index, enrichment factor, pollution load index, and metal pollution index). Moreover, the potential toxic effects were assessed on the basis of sediment quality standards (threshold effect concentration, probable effect concentration, midpoint effect concentration) and the toxic risk index. Values of the pollution load index and metal pollution index showed that the ecological risk related to the presence of HMs in the river bottom sediments was high in both rivers. It was observed that in most of the samples the concentrations of HMs were under the probable effect concentration value, defined as the limit above which a toxic effect on aquatic organisms can be expected. However, Ni and Zn concentrations also exceeded the PEC level by 11.76% and 16% for the Oder and Vistula rivers, respectively. Assessment of the EF index values for the Oder and Vistula rivers demonstrated that Cr and Zn have generally greater enrichment compared to other heavy metals. Cluster analysis and principal component analysis showed that the spatial distribution of HMs in sediments is mainly related to point sources of pollution, and is modified by the river fluvial process. The direction of pollution distribution in sediments is opposite in the two analyzed rivers. The Oder River shows higher concentrations from downstream to upstream, caused by the presence of point and area sources of pollution. In the case of the Vistula River, pollution decreases from downstream to upstream, which may be an example of sediment deposition in reservoirs located along the river, working modern sewage treatment plants in big cities, and river self-purification processes.

Journal ArticleDOI
01 Jan 2022-Catena
TL;DR: In this paper , the authors explored the time-lag and time-accumulation effects of the NDVI response to climate factors (precipitation, temperature), identified the main controlling factors that influence the variation of NDVI, and found that considering the optimal time effect is of great significance.
Abstract: • The increase trend of NDVI in the northwest is greater than that in the southeast. • NDVI fluctuated greatly and the growth rate was greater at low-altitude. • Vegetation in the QLMs is mainly affected by the time-accumulation of PRE and the time-lag of TMP. • Compared with PRE, TMP was the dominant factor of the greening in the QLMs. Understanding the trend of vegetation change and its reaction to climate variation is important for revealing the mechanism of ecosystem behavior. However, current research rarely systematically analyzes the time effects of climate variation on vegetation dynamics (time-lag and time-accumulation effects), especially in arid and semi-arid mountainous terrain. The typical mountainous terrain—the Qilian Mountains was taken as the study area, and the spatiotemporal changes and vertical zonality distributions of the normalized vegetation index (NDVI) were explored. This study explored the time-lag and time-accumulation effects of the NDVI response to climate factors (precipitation, temperature), identified the main controlling factors that influence the variation of NDVI. The results show that in the growing season from 2000 to 2019, the NDVI represented an overall upward trend, especially in the northwest, and the growth rate of NDVI at low-altitude was greater. The time-accumulation effect of precipitation has an obvious effect on vegetation, especially on deserta and meadow; and the time-lag and time-accumulation effects of temperature have an obvious influence. Regarding the climate-vegetation response mechanism, this study finds that considering the optimal time effect is of great significance. In addition, compared with precipitation, the temperature has a more significant promotion effect on vegetation growth in the Qilian Mountains. The above results indicate that when the existing climate models study vegetation-climate interactions, considering the time effects of vegetation response to climate is of great significance for accurately monitoring vegetation dynamics under environmental changes.

Journal ArticleDOI
01 Jun 2022-Catena
TL;DR: Wang et al. as mentioned in this paper investigated the impact of landscape factors on soil microorganisms and found that soil fungal diversity increased with habitat fragmentation, although soil bacterial richness did not change significantly.
Abstract: Habitat fragmentation is a primary cause of biodiversity loss. As an essential part of the ecosystem, soil microorganisms participate in a series of ecosystem processes. However, the role of landscape factors on soil microorganisms is not well understood. Based on high-throughput sequencing of soil samples at three depths (0–10 cm, 10–20 cm, 20–30 cm) from 30 landscape sites along a habitat fragmentation gradient, we calculated soil bacterial and fungal diversity in the agro-pastoral ecotone of northern China. We then investigated the impact of climatic factors, soil characteristics, and landscape context (patch density, edge density, mean patch size, and mean nearest-neighbor distance) on soil bacterial and fungal diversity. We found that soil fungal richness increased with habitat fragmentation (patch density and edge density), although soil bacterial richness did not change significantly. Soil bacterial and fungal community composition both changed with habitat fragmentation. Soil characteristics were key factors determining soil bacterial diversity, especially in the 10–20 cm soil depth. Soil fungal diversity was closely related to landscape context, showing a significant positive correlation with patch density and edge density, and a significant negative correlation with mean patch size. Structural equation modeling showed that landscape factors directly affected soil fungal diversity but indirectly affected soil bacterial diversity by changing soil characteristics. We highlight that soil fungal diversity shows an increasing trend with increased habitat fragmentation. Landscape context plays a stronger role in maintaining soil fungal diversity than soil characteristics and climatic factors.

Journal ArticleDOI
01 Jan 2022-Catena
TL;DR: In this article, the authors investigated the capability of a state-of-the-art model developed using the group method of data handling (GMDH) to spatially model landslide susceptibility.
Abstract: Landslide susceptibility (LS) mapping is an essential tool for landslide risk assessment. This study aimed to provide a new approach with better performance for landslide mapping and adopting readily available variables. In addition, it investigates the capability of a state-of-the-art model developed using the group method of data handling (GMDH) to spatially model LS. Furthermore, hybridized models of GMDH were developed using different metaheuristic algorithms. The study area was the Bonghwa region of South Korea, for which an accurate landslide inventory dataset is available. We considered a total of 13 spatial covariates (altitude, slope, aspect, topographic wetness index, valley depth, plan curvature, profile curvature, distance from fault, distance from river, distance from road, land use, density of forest, and lithology were chosen as independent variables). Two benchmark models—random forest and boosted regression trees—were used to compare their results with the standalone GMDH and hybridized models. We compared model accuracy using the two most robust evaluation metrics, root mean square error (RMSE) and area under the receiver operating characteristic curve (AUROC). The validation results showed that hybridized models outperformed the standalone GMDH model. Moreover, the hybridized GMDH-PSO (AUC = 0.83, RMSE = 0.108), GMDH-IWO (AUC = 0.81, RMSE = 0.111), GMDH-BBO (AUC = 0.8; RMSE = 0.12), and GMDH-ICA (AUC = 0.8; RMSE = 0.117) had a better predictive performance than both RF and BRT. Therefore, the proposed approach could successfully produce landslide susceptibility maps using relatively few readily available variables and can be repeated in data-scarce regions.

Journal ArticleDOI
01 Mar 2022-Catena
TL;DR: In this paper , the authors calculated the rainfall erosivity from 1965 to 2019, based on daily precipitation data, for 17 watersheds on the Loess Plateau, and analyzed the temporal and spatial variation of rainfall erosity, and assessed the impact of rainfall erosion changes on sediment load in these typical watersheds.
Abstract: • Rainfall erosivity decreased from southeast to northwest in the Loess Plateau. • ENSO and sunspots had influence on rainfall erosivity in the Loess Plateau. • Rainfall erosivity and check dam mainly caused sediment load changes before 1980. • Vegetation was the main reason of sediment load changes after 1999. Rainfall erosivity is one of the key dynamic factors leading to water erosion, which causes widespread soil erosion worldwide. This study calculated the rainfall erosivity from 1965 to 2019, based on daily precipitation data, for 17 watersheds on the Loess Plateau. The data were also used to analyze the temporal and spatial variation of rainfall erosivity, and assess the impact of rainfall erosivity changes on sediment load in these typical watersheds. The possible causes of rainfall erosivity and sediment load changes are also discussed. The results of the study revealed that on different time scales, the spatial distribution of rainfall erosivity showed a pattern of decreasing from southeast to northwest in the Loess Plateau. Moreover, the rainfall erosivity measured by some weather stations increased significantly in May, June, and September ( p < 0.05). Additionally, the changes brought on by ENSO and sunspots had a specific influence on the changes of rainfall erosivity in the Loess Plateau. Furthermore, the sediment load in the typical watersheds of the Loess Plateau showed a significant decreasing trend in yearly and monthly time scales ( p < 0.05). Before 1980, the change in rainfall erosivity was an important reason for the change of sediment load and the construction of backbone check dams also intercepted a large amount of sediment. However, from 1980 to 1998, the interception effect of backbone check dams and the increase in vegetation together caused sediment load changes. After 1999, the restoration of vegetation was the main factor instigating a further reduction in sediment load. Studying the changes in the rainfall erosivity will provide a useful reference for future ecological construction and soil erosion control in the Loess Plateau.

Journal ArticleDOI
01 Feb 2022-Catena
TL;DR: In this paper, the authors measured microbial metabolic limitation, microbial CUE, and extracellular enzyme activities along an estuarine salinity gradient ranging from freshwater (0.1 ǫ −0.38 ) to oligohaline (2.1ǫ -0.5 ) in a tidal wetland.
Abstract: Sea level rise–induced salinization is projected to influence the decomposition of soil carbon (C) in tidal wetlands. Despite evidence showing that microbial metabolism can determine the fate of soil C decomposition, the response of microbial metabolism to salinization in tidal wetlands remains largely unknown. Microbial metabolism is impacted by the microbial metabolic limitation and carbon use efficiency (CUE), which can provide mechanistic insights into soil C decomposition. Here, we measured microbial metabolic limitation, microbial CUE, and extracellular enzyme activities along an estuarine salinity gradient ranging from freshwater (0.1 ± 0.1 mg g−1) to oligohaline (2.1 ± 0.5 mg g−1) in a tidal wetland. Overall, microorganisms were limited by phosphorus (P) in the tidal wetlands, where salinization increased microbial P limitation by 34%. The enhanced microbial P limitation was attributed to increases in soil C:P and belowground biomass, as well as decreases in root C:P with increasing salinity. Microbial CUE decreased from 0.38 to 0.33 as salinity increased, while microbial P limitation was negatively correlated with microbial CUE, representing the trade-off between the two. Furthermore, microbial P limitation was positively associated with C-, nitrogen (N)-, and P-acquiring extracellular enzyme activities, while all these enzyme activities were negatively correlated with microbial CUE. These results illustrate that to balance microbial P limitation with salinization, microorganisms transfer more energy from the microbial CUE to extracellular enzyme production, and this was the mechanism underlying the trade-off. Microorganisms were also limited by C in the tidal wetlands. However, as the increasing belowground biomass alleviated microbial C limitation with salinization, no relationship was observed between microbial C limitation and CUE. Future C and nutrient models aimed to simulate tidal wetland ecosystem responses to salinization will benefit from the inclusion of trade-off between microbial CUE and microbial P limitation for more accurate prediction.

Journal ArticleDOI
01 Oct 2022-Catena
TL;DR: Wang et al. as mentioned in this paper assessed the increment and change rate of fractional vegetation cover (FVC) in 2000-2020 in China and compared the influences of land types and major ecological projects to assess the vegetation-human nexus.
Abstract: Vegetation-coverage research shows China’s significant 25% contribution to global greening. Vegetation is the link between water, soil and atmosphere, making it an important indicator of changes in anthropogenic factors. Anthropogenic attribution analysis of vegetation change helps us to identify and estimate the relationship between vegetation change and major ecological projects, and their corresponding relationship may be the antecedents and consequences of vegetation dynamics. This study assessed the increment and change rate of fractional vegetation cover (FVC) in 2000–2020 in China. The influences of land types and major ecological projects were systematically compared to assess the vegetation-human nexus. China has experienced progressive greening in the study period with regional variations in patterns and causes. The FVC changes and spatio-temporal variations were induced by notable human activities such as land-use conversion, China’s afforestation programs (CAP), Ant Forest Project (AFP), and Conversion of Cropland to Forest Program (CCFP). The North region recorded the highest change rate. The area changes in cropland, grassland, and forest were the main FVC drivers. The average FVC change rate of CAP changed very high in 21 years. The AFP exerted significant impacts on FVC changes. The CCFP effectively promoted FVC improvements from the low, medium and high grades to the very high coverage grade. These comparative trends illustrated the intricate relationships between anthropogenic factors and greening. The findings could enhance the prediction and evaluation of vegetation-cover dynamics under anthropogenic changes and the implementation and management of afforestation programs.

Journal ArticleDOI
01 Jul 2022-Catena
TL;DR: Wang et al. as mentioned in this paper studied 12 black soil profiles and used 137 Cs analysis to estimate erosion rates, which showed a gradual decrease in erosion rate from west (>3 mm/a) to east (0-3 mm /a) across the black soil region.
Abstract: • Erosion rate of black soil in Northeast China decreases gradually from west to east. • The erosion rate over the black soil region is 2.22 mm/a on average. • Wind erosion is dominant in the western black soil region. • Water erosion is dominant in the eastern black soil region. • We suggest planting herbs in the west and afforestation in the east to reduce erosion. Northeast China is one of the three largest black soil regions on Earth; however, although the black soil of the region is undergoing severe erosion, the dominant erosional agent (wind or water) remains unclear. We studied 12 black soil profiles and used 137 Cs analysis to estimate erosion rates. The results, combined with previously published data, show a gradual decrease in erosion rate from west (>3 mm/a) to east (0–3 mm/a) across the black soil region. Correlation of the erosion rates and climatic parameters suggests that wind erosion dominates in the west and water erosion dominates in the east. Using the mean erosion rate (2.22 mm/a) as a reference, the black soil will be completely eroded in ∼113 years. Given the agricultural importance of the black soil region, we suggest that tillage should be reduced throughout the region, and that to control ongoing erosion, revegetation should be conducted by planting herbs in the west and afforestation in the east.

Journal ArticleDOI
01 Oct 2022-Catena
TL;DR: Wang et al. as discussed by the authors identified the essential conditioning factors of landslides to increase the predictive ability of landslide susceptibility models and explore the effects of different grid resolutions (i.e., 30 m, 300 m, 1000 m, 2000 m, and 3000 m) on landslide susceptibility assessment.
Abstract: This study attempts to identify the essential conditioning factors of landslides to increase the predictive ability of landslide susceptibility models and explore the effects of different grid resolutions (i.e., 30 m, 300 m, 1000 m, 2000 m, and 3000 m) on landslide susceptibility assessment. Firstly, taking Wushan and Wuxi counties in Chongqing as an example, a geospatial dataset comprising 1137 historical landslide locations and preliminary 28 conditioning factors was randomly divided into training (70%) and testing (30%) datasets at each grid resolution. Secondly, spearman correlation coefficient (SCC), recursive feature elimination (RFE) and their combination (SCC-RFE) were chosen to identify the essential conditioning factors out of 28 original factors at five grid resolutions. Subsequently, random forest (RF) model was used to construct landslide susceptibility model with the original and essential conditioning factors, respectively. Finally, the reasonableness of the essential conditioning factors was verified by comparing the receiver operation characteristic (ROC) curves (AUC) and other statistical signifiers in multiple grid resolutions. Results show that: (1) Average annual rainfall, elevation, lithology and POI have a significant impact on the occurrence of landslides, while NDVI and land cover has little effect on the occurrence of landslides in Wushan and Wuxi counties. (2) The primary essential factors (i.e., elevation, rainfall) are less affected by the grid resolution, while the subdominant factors (i.e., DEM-derived factors, human activity factors) are strongly influenced. (3) SCC-RFE-RF model performs best with the screened essential conditioning factors at a grid resolution smaller than 2000 m, which indicates choosing the essential conditioning factors or optimum grid resolution can guarantee greater accuracy of landslide susceptibility models. This study provides a reference for future analysis in selecting landslide conditioning factors and grid resolutions.

Journal ArticleDOI
01 Feb 2022-Catena
TL;DR: Zhang et al. as mentioned in this paper performed a comprehensive analysis of the source-specific ecological risk assessment of metal(loid)s in a typical mangrove wetland in the Jiulong River Estuary.
Abstract: • A combination of source apportionment and ecological risk assessment was performed. • Source apportionment was conducted using APCS-MLR. • Hg and Cd are the primary factors for the Nemerow integrated risk index. • Aquaculture makes significant contribution to the ecological risks of Hg and Cd. Metal(loid)s in mangrove sediments could pose noticeable ecological risks, but studies on source-specific ecological risks of metal(loid)s in sediments are limited. In this study, surface sediments collected from a typical mangrove wetland in the Jiulong River Estuary were used to perform a comprehensive analysis of the source-specific ecological risk assessment of metal(loid)s. Cd, Hg, Mn, and Cu were the metal(loid)s with large coefficient of variation (CV) values and were more likely to be affected by human activities. Principal component analysis (PCA) first identified four potential sources, namely, natural, coal combustion and industrial, aquacultural, and agricultural sources, of metal(loid)s in surface sediments. The absolute principal component score-multiple linear regression (APCS-MLR) was then apportioned for its contributions to each metal(loid). The results showed that natural sources dominated the total metals, with a contribution of 62.7%, and coal combustion and industrial sources and aquaculture sources were the two major anthropogenic sources, with contributions of 19.26% and 17.50%, respectively. Three indices, the geoaccumulation index ( I geo ), pollution index ( PI ), and potential ecological risk of individual factors ( E r i ), clearly indicated that Hg and Cd showed much higher ecological risks than the other metals. Additionally, the integrated ecological risk could be more appropriately assessed by the Nemerow integrated risk index ( NIRI ), which showed no less than moderate ecological risks. More importantly, for the source-specific ecological risk assessment, the aquacultural source was identified to be a significant contributor to the ecological risk of Hg and Cd. In conclusion, the source-specific ecological risk rather than the sole source apportionment or ecological risk assessment could provide more information for the targeted environmental management of sediments contaminated by metal(loid)s.

Journal ArticleDOI
Yu Yan1, Rui-an Wan1, Ruilian Yu1, Gong-Ren Hu1, Chengqi Lin, Huabin Huang 
01 Feb 2022-Catena
TL;DR: Zhang et al. as discussed by the authors performed a comprehensive analysis of the source-specific ecological risk assessment of metal(loid)s in surface sediments collected from a typical mangrove wetland in the Jiulong River Estuary, and the absolute principal component score-multiple linear regression (APCS-MLR) was then apportioned for its contributions to each metal.
Abstract: Metal(loid)s in mangrove sediments could pose noticeable ecological risks, but studies on source-specific ecological risks of metal(loid)s in sediments are limited. In this study, surface sediments collected from a typical mangrove wetland in the Jiulong River Estuary were used to perform a comprehensive analysis of the source-specific ecological risk assessment of metal(loid)s. Cd, Hg, Mn, and Cu were the metal(loid)s with large coefficient of variation (CV) values and were more likely to be affected by human activities. Principal component analysis (PCA) first identified four potential sources, namely, natural, coal combustion and industrial, aquacultural, and agricultural sources, of metal(loid)s in surface sediments. The absolute principal component score-multiple linear regression (APCS-MLR) was then apportioned for its contributions to each metal(loid). The results showed that natural sources dominated the total metals, with a contribution of 62.7%, and coal combustion and industrial sources and aquaculture sources were the two major anthropogenic sources, with contributions of 19.26% and 17.50%, respectively. Three indices, the geoaccumulation index (Igeo), pollution index (PI), and potential ecological risk of individual factors ( E r i ), clearly indicated that Hg and Cd showed much higher ecological risks than the other metals. Additionally, the integrated ecological risk could be more appropriately assessed by the Nemerow integrated risk index (NIRI), which showed no less than moderate ecological risks. More importantly, for the source-specific ecological risk assessment, the aquacultural source was identified to be a significant contributor to the ecological risk of Hg and Cd. In conclusion, the source-specific ecological risk rather than the sole source apportionment or ecological risk assessment could provide more information for the targeted environmental management of sediments contaminated by metal(loid)s.

Journal ArticleDOI
01 Mar 2022-Catena
TL;DR: In this paper , the authors performed laboratory disintegration tests on granite residual soil taken from the red soil, sandy soil, and detritus layers of a collapsing gully, and the disintegration behaviour was quantified by defining a disintegration ratio, R d , and three equivalent disintegration rates, v I , v II , and v III , corresponding to R d = 10, 30, and 50%, respectively.
Abstract: • Cross-disciplinary work that explains a collapsing gully’s formation mechanism. • Collapsing gully’s material source is systematically studied from a geotechnical perspective. • Soil disintegration process is quantified. • Weakening of cementation is shown to be responsible for disintegration and collapsing gully. Recently, there has been an increase in collapsing gullies in the south of China as one of the most destructive types of soil erosion. Most collapsing gullies are formed on a well-developed granite crust; thus, granite residual soil plays a critical role. However, the extent to which the geotechnical features of residual soil, especially soil disintegration, affects collapsing gully formation is poorly understood. This study performed laboratory disintegration tests on granite residual soil taken from the red soil, sandy soil, and detritus layers of a collapsing gully. The disintegration behaviour was quantified by defining the disintegration ratio, R d , and three equivalent disintegration rates, v I , v II , and v III , corresponding to R d = 10%, 30%, and 50%, respectively. The results revealed that the red soil layer (depth < 1.3 m) and the soil at the shallower depth of the sandy soil layer (depth < 3.0 m) showed similar disintegration behaviours, which were complete ( R d = 100%) and rapid, with v I values in the range of 66.7–266.7%/min. The soil disintegration in the sandy soil layer was characterised by an incremental increase in R d to 100% within 100 min. The residual soil at the bottom of the sandy soil layer and the top of the detritus layer (depths of 4.0–8.0 m) disintegrated consistently at the first, after which the disintegration rate gradually decreased with v III lower than 1%/min. The detritus layer soil at a depth greater than 10.0 m showed incomplete disintegration, and the ultimate R d was approximately 60%. The formation mechanism for the soil disintegration and gully collapse was also proposed. The weakening of cementation triggered the breakup of soil aggregates and led to soil disintegration and the occurrence of a gully collapse. This study provided new insights on gully erosion.

Journal ArticleDOI
01 Mar 2022-Catena
TL;DR: In this paper, the variability of soil physicochemical parameters and fertility estimates in three types of semi-arid steppe rangelands of North Africa, viz. Stipa tenacissima, Artemisia herba-alba and Atriplex halimus, were investigated.
Abstract: Soil fertility depends on vegetation cover, climatic conditions and soil-specific edaphic factors that regulate transformation processes of plant residues and organic matter. Soil physicochemical characteristics in drylands negatively affect evolutionary process of soil materials resulting in fertility loss. This study investigated the variability of soil physicochemical parameters and fertility estimates in three types of semi-arid steppe rangelands of North Africa, viz. Stipa tenacissima, Artemisia herba-alba and Atriplex halimus. The effect of soil parameters on the evolution of soil fertility was appraised using soil organic carbon (SOC), available phosphorus (AP) and C:P ratio as fertility indicators. In two semi-arid regions with haplic calcisols, soil was sampled in six replicates at each steppe rangeland and a control (bare soil). Using standard protocols, each sample was analyzed to determine pH, electrical conductivity (EC), SOC, AP, C:P ratio, total and active CaCO3. All the soil physicochemical parameters tested, except total CaCO3, showed positive increases in A. halimus and S. tenacissima steppe rangelands. The variation of pH and EC values among rangelands was significant, with A. halimus rangelands had significantly the highest scores and A. herba-alba rangelands the lowest scores. The redundancy analysis showed that the edaphic factors triggering significant increases in scores of soil fertility indicators, when compared to the control, were active CaCO3, EC and pH. These physicochemical parameters positively determined the accumulation of AP and SOC, especially in A. halimus rangelands. The high values of stochiometric C:P ratio were associated to soil characteristics of S. tenacissima and A. herba-alba rangelands. Our findings suggest that soil physicochemical parameters of semi-arid steppe rangelands - compared to bare soil - influenced the evolution of soil fertility and stoichiometric C:P ratio. The type of steppe vegetation differently affects the physicochemistry and stoichiometry of the soil.

Journal ArticleDOI
01 Mar 2022-Catena
TL;DR: In this article , the variability of soil physicochemical parameters and fertility estimates in three types of semi-arid steppe rangelands of North Africa, viz. Stipa tenacissima, Artemisia herba-alba and Atriplex halimus, were investigated.
Abstract: Soil fertility depends on vegetation cover, climatic conditions and soil-specific edaphic factors that regulate transformation processes of plant residues and organic matter. Soil physicochemical characteristics in drylands negatively affect evolutionary process of soil materials resulting in fertility loss. This study investigated the variability of soil physicochemical parameters and fertility estimates in three types of semi-arid steppe rangelands of North Africa, viz. Stipa tenacissima, Artemisia herba-alba and Atriplex halimus. The effect of soil parameters on the evolution of soil fertility was appraised using soil organic carbon (SOC), available phosphorus (AP) and C:P ratio as fertility indicators. In two semi-arid regions with haplic calcisols, soil was sampled in six replicates at each steppe rangeland and a control (bare soil). Using standard protocols, each sample was analyzed to determine pH, electrical conductivity (EC), SOC, AP, C:P ratio, total and active CaCO3. All the soil physicochemical parameters tested, except total CaCO3, showed positive increases in A. halimus and S. tenacissima steppe rangelands. The variation of pH and EC values among rangelands was significant, with A. halimus rangelands had significantly the highest scores and A. herba-alba rangelands the lowest scores. The redundancy analysis showed that the edaphic factors triggering significant increases in scores of soil fertility indicators, when compared to the control, were active CaCO3, EC and pH. These physicochemical parameters positively determined the accumulation of AP and SOC, especially in A. halimus rangelands. The high values of stochiometric C:P ratio were associated to soil characteristics of S. tenacissima and A. herba-alba rangelands. Our findings suggest that soil physicochemical parameters of semi-arid steppe rangelands - compared to bare soil - influenced the evolution of soil fertility and stoichiometric C:P ratio. The type of steppe vegetation differently affects the physicochemistry and stoichiometry of the soil.

Journal ArticleDOI
01 Nov 2022-Catena
TL;DR: In this article , the effects of forest disturbances on soil erosion dynamics in a peri-urban forest of Northern Greece, between 1995 and 2020, were assessed by coupling the Google Earth Engine (GEE) cloud-computing platform and the RUSLE erosion prediction model.
Abstract: • RUSLE implementation in a cloud-based (GEE) platform. • Remote sensing data were utilized to capture the burned area perimeter and loggings of damaged trees by the insect infestations. • Erosion response to abiotic and biotic disturbances. • Multi-temporal monitoring erosion in Peri-Urban Forest. During the last decades, the demographic trajectory and the associated urban development have increased the demand for urban and peri -urban green space. Peri-urban forests provide a wide range of goods and services to city dwellers critical to human well-being. However, these ecosystems are particularly vulnerable to biotic and abiotic stresses. This study aimed to assess the effects of forest disturbances on soil erosion dynamics in a peri -urban forest of Northern Greece, between 1995 and 2020. Monitoring of soil erosion dynamic was performed by coupling the Google Earth Engine (GEE) cloud-computing platform and the RUSLE erosion prediction model. The results highlighted the potential of Landsat imagery to efficiently delineate through time forest cover changes due to the influence of biotic and abiotic factors. After a major fire event, average soil erosion showed an increase of 7.7 t/ha/year whilst the emergency hillslope rehabilitation treatments led to a decrease equal to 8.9 t/ha/year. On the contrary, post-fire watershed stabilization measures and selective logging for the removal of the infested individual trees had a negligible effect on soil loss. The temporal changes in soil loss rate are not only justified by forest cover changes but also by the variability in rainfall, which is also considered a dynamic factor of RUSLE model. Monitoring of soil loss and erosion regulation services over peri -urban forests provide essential information for policy-making and management of these valuable natural resources.

Journal ArticleDOI
01 Oct 2022-Catena
TL;DR: In this paper , a long-term approach to quantify surface runoff and soil loss generated by different land use and precipitation regimes on Pisha sandstone hillslopes from the Loess Plateau, China was carried out.
Abstract: In this research, a long-term (2014–2020) approach to quantify surface runoff and soil loss generated by different land use and precipitation regimes on Pisha sandstone hillslopes from the Loess Plateau, China was carried out. Using the K-means clustering algorithm, 50 precipitation events were classified into three different regime types based on depth, duration, and maximum 30-minute intensity. Permutational multivariate analysis of variance (PERMANOVA) and Principal component analysis (PCA) were used to test clustering rationality. Our results suggest that runoff coefficient (RC) and soil loss (SL) exhibited significant differences depending on precipitation regimes: heavy storms (precipitation regime II) were found to induce the largest surface runoff and soil loss, followed by precipitation regimes I and III. RC and SL were also found with significant differences (p < 0.05) related to different land-use types. Mean RC and erosion rates among the six land-use types analyzed varied as bare land > cropland > artificial grassland > native grassland > shrubland > forestland. Results of this study suggest more attention should be paid to vegetation selection and land-use type depending on precipitation regimes in Pisha sandstone morphologies. Accordingly, forestland and shrubland should be the first choices to control soil erosion when land-use conversion is implemented, whereas bare land, croplands, or artificial grassland (e.g., alfalfa) should be carefully considered and future vegetation restoration policies should assess the proper re-vegetation type before any action may be initiated.

Journal ArticleDOI
01 Jan 2022-Catena
TL;DR: In this article, the authors investigated the effects of plant communities on soil C, N, P contents and their stoichiometry on steep gully slopes and found that grassland had higher soil total carbon, soil total nitrogen, soil overall phosphorus, and stoichiometries than shrubland.
Abstract: The contents and stoichiometric characteristics of soil carbon, nitrogen and phosphorus greatly influence the structures and functions of the ecological system. The steep gully slope is the main source of erosion sediment in the small watershed on the Loess Plateau, China. It is vital to fully quantify the potential effects of vegetation restoration on soil nutrients and their stoichiometry under such harsh standing environments. To investigate the effects of plant communities on soil C, N, P contents and their stoichiometry on steep gully slopes. Two shrubs (Hippophae rhamnoides Linn., Caragana korshinskii Kom.), four typical grasses (Carex lanceolata Boott., Artemisia giraldii Pamp., Bothriochloa ischcemum (Linn.) Keng. and Artemisia sacrorum Ledeb.), and one slope farmland planted with Zea mays (as control) were chosen as the testing sites. Soil samples were collected from seven layers (0–10, 10–20, 20–30, 30–40, 40–60, 60–80, and 80–120 cm). The results showed that grassland had higher soil total carbon, soil total nitrogen, soil total phosphorus, and stoichiometries than shrubland on steep gully slopes. The C/N ratio had the minimum variation between different communities, while N/P and C/P ratios gradually decreased with soil depth. Both N and P were scarce on steep gully slopes. Soil C, N, P contents and their stoichiometry were closely related to bulk density, plant litter density and root mass density. The results are helpful to understand the spatial variation of soil nutrients and their stoichiometry and vegetation management on steep gully slopes in arid and semi-arid regions.

Journal ArticleDOI
01 Feb 2022-Catena
TL;DR: Wang et al. as discussed by the authors proposed a regional-scale high spatial resolution (30 m) SOM mapping method based on multitemporal synthetic images, which is suitable for black soil areas in Northeast China and extends the application of GEE in digital soil mapping.
Abstract: Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development, especially in fertile black soil areas. The present study proposed a regional-scale high spatial resolution (30 m) SOM mapping method based on multitemporal synthetic images. The study area is located on the Songnen Plain of Northeast China. First, all available Landsat 8 surface reflectance (SR) data during the bare soil period (April and May) from 2014 to 2019 in the study area were screened in the Google Earth Engine (GEE), and the cloud mask was constructed. The median, average, maximum, and minimum values of the image set were synthesized according to single-year multimonth, multiyear single-month and multiyear multimonth time ranges, and the spectral index of the synthesized image was constructed. Second, the bands and spectral indices of different synthetic images were used as input to establish a random forest (RF) model of SOM prediction, and the accuracies of different spatial prediction models of SOM were compared to evaluate the optimal regional remote sensing prediction model of SOM. The following results were show. 1) The use of the spectral index combined with the image band as input had a greater improvement in the accuracy of SOM prediction than the use of only the image band. 2) Compared to the average, maximum and minimum synthesized images, the median synthesized image had higher accuracy in SOM prediction. 3) More years of synthesized images provided more robust SOM prediction results. 4) May was the best time window for SOM mapping on the Songnen Plain. This study presents a large-scale and high spatial resolution SOM mapping method that is suitable for black soil areas in Northeast China and extends the application of GEE in digital soil mapping.

Journal ArticleDOI
01 Feb 2022-Catena
TL;DR: Wang et al. as discussed by the authors proposed a regional-scale high spatial resolution (30 m) SOM mapping method based on multitemporal synthetic images, which is suitable for black soil areas in Northeast China and extends the application of GEE in digital soil mapping.
Abstract: Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development, especially in fertile black soil areas. The present study proposed a regional-scale high spatial resolution (30 m) SOM mapping method based on multitemporal synthetic images. The study area is located on the Songnen Plain of Northeast China. First, all available Landsat 8 surface reflectance (SR) data during the bare soil period (April and May) from 2014 to 2019 in the study area were screened in the Google Earth Engine (GEE), and the cloud mask was constructed. The median, average, maximum, and minimum values of the image set were synthesized according to single-year multimonth, multiyear single-month and multiyear multimonth time ranges, and the spectral index of the synthesized image was constructed. Second, the bands and spectral indices of different synthetic images were used as input to establish a random forest (RF) model of SOM prediction, and the accuracies of different spatial prediction models of SOM were compared to evaluate the optimal regional remote sensing prediction model of SOM. The following results were show. 1) The use of the spectral index combined with the image band as input had a greater improvement in the accuracy of SOM prediction than the use of only the image band. 2) Compared to the average, maximum and minimum synthesized images, the median synthesized image had higher accuracy in SOM prediction. 3) More years of synthesized images provided more robust SOM prediction results. 4) May was the best time window for SOM mapping on the Songnen Plain. This study presents a large-scale and high spatial resolution SOM mapping method that is suitable for black soil areas in Northeast China and extends the application of GEE in digital soil mapping.

Journal ArticleDOI
01 Feb 2022-Catena
TL;DR: In this article , the authors investigated the variations in soil microbial resource limitation to changing precipitation regimes and edaphic factors, particularly for a highly divergent soil organic C gradient along the transect from dry to wet areas.
Abstract: Soil extracellular enzyme stoichiometry has been used to characterize the acquisition strategies of soil microorganisms in obtaining carbon (C), nitrogen (N) and phosphorus (P). However, the variations in soil microbial resource limitation to changing precipitation regimes and edaphic factors remain poorly understood, particularly for a highly divergent soil organic C gradient along the transect from dry to wet areas. This study investigated soil microbial C and nutrient (P/N) limitations along a 3,000-km humidity gradient. The results revealed a downward unimodal-shaped relationship between the humidity index (HI) and soil microbial P/N limitation with a threshold of HI = 0.68 (corresponding mean annual precipitation ranged from 469 mm to 551 mm). Changes in microbial C limitation, and total- and available soil P contents along the humidity gradient further revealed the presence of this threshold. Soil microbial C limitation remained constant at a humidity level below HI = 0.68, and it increased above this threshold. Microbial P limitation decreased and N limitation increased as humidity increased to HI = 0.68. Above HI = 0.68, the microbial P limitation gradually elevated with an increase in humidity. We also found that humidity and soil nutrients are critical factors explaining the variations in microbial resource limitation, and soil nutrients control microbial resource limitation on either side of the HI = 0.68 threshold. These findings suggest that the acquisition of N and P by soil microorganisms stimulates the decomposition of soil organic matter, and future predictions of ecosystem C budgets should thus consider enzymatic processes.